class_docstr: A LearningRateSchedule that uses an delayed cosine decay schedule.
docstr: Applies delayed cosine decay to the learning rate.
  
  Args:
    initial_learning_rate: A scalar `float32` or `float64` `Tensor` or a
      Python number.  The initial learning rate.
    constant_learning_rate: A scalar `float32` or `float64` `Tensor` or a
      Python number.  The initial learning rate.
    end_learning_rate: A scalar `float32` or `float64` `Tensor` or a
      Python number.  The minimal end learning rate.
    warmup_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
      Must be positive.  See the decay computation above.
    start_decay_step: A scalar `float32` or `float64` `Tensor` or a
      Python number.  The decay start start.
    decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
      Must be positive.  See the decay computation above.
    name: String.  Optional name of the operation.  Defaults to
      'ExponentialDecay'.
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code: def __init__(
      self,
      initial_learning_rate,
      constant_learning_rate,
      end_learning_rate,
      warmup_steps,
      start_decay_step,
      decay_steps,
      name=None,
  ):
      """Applies delayed cosine decay to the learning rate.
  
      Args:
        initial_learning_rate: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The initial learning rate.
        constant_learning_rate: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The initial learning rate.
        end_learning_rate: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The minimal end learning rate.
        warmup_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
          Must be positive.  See the decay computation above.
        start_decay_step: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The decay start start.
        decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
          Must be positive.  See the decay computation above.
        name: String.  Optional name of the operation.  Defaults to
          'ExponentialDecay'.
      """
      super(WarmupDelayedCosineDecay, self).__init__()
      self.initial_learning_rate = initial_learning_rate
      self.constant_learning_rate = constant_learning_rate
      self.end_learning_rate = end_learning_rate
      self.warmup_steps = warmup_steps
      self.start_decay_step = start_decay_step
      self.decay_steps = decay_steps
      self.name = name
  
file_path: graphics/tensorflow_graphics/projects/points_to_3Dobjects/losses/learning_rate_schedule.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import tensorflow
label_desc: Use image node_id 1 to create a new WarmupDelayedCosineDecay object from inherited base classes: tf.keras with example: obj = WarmupDelayedCosineDecay(initial_learning_rate, constant_learning_rate, end_learning_rate, warmup_steps, start_decay_step, decay_steps, name)